Personalized POI Embedding for Successive POI Recommendation with Large-scale Smart Card Data

Point-of-interest (POI) recommendation can help providing better user experience, and provide users with third-party information about restaurant or entertainment. There are several studies to predict the next POI where the user will go so as to recommend appropriate services. They use additional information such as text or location for more precise prediction, or manually define user patterns. However, it is costly to collect and analyze large amounts of data for POI recommendation. In this paper, we propose a novel method to recommend POI by extracting the personalized movement pattern only from the POI data without any additional information. We collected POI data ofl. 5M users for six months from smart card, and produce personalized POI and user embedding. Since it is hard to construct one POI recommendation model for 1.5 million people, we divide them to several groups according to their simple mobility pattern. Given a previous POI sequence, user and group id, the proposed model is trained to maximize the probability of the next POI. Although the learning method of the proposed model is simple, even if the given POI sequence is the same, successive POI can be predicted differently according to the user, resulting in personalized POI recommendation. The proposed model achieves 73.64%, 88.65%, and 91.54% in top-1, 3 and 5 accuracies which are higher than the performance of the baseline model (59.48%, 75.85%, and 80.1%, respectively). Besides, we verify the embedding performance of the proposed model through arithmetic operations between POI vectors.